KronoMiner: using multi-foci navigation for the visual exploration of time-series data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Augmented visualization with natural feature tracking
Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia
Lin-spiration: using a mixture of spiral and linear visualization layouts to explore time series
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
International Journal of Human-Computer Studies
Comparing averages in time series data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Temporal visualization of boundary-based geo-information using radial projection
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Interactive horizon graphs: improving the compact visualization of multiple time series
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evaluation of alternative glyph designs for time series data in a small multiple setting
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fisheye word cloud for temporal sentiment exploration
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Evaluating the efficiency of physical visualizations
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Storygraph: extracting patterns from spatio-temporal data
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
Finding anomalies in time-series using visual correlation for interactive root cause analysis
Proceedings of the Tenth Workshop on Visualization for Cyber Security
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Line graphs have been the visualization of choice for temporal data ever since the days of William Playfair (1759-1823), but realistic temporal analysis tasks often include multiple simultaneous time series.In this work, we explore user performance for comparison, slope, and discrimination tasks for different line graph techniques involving multiple time series.Our results show that techniques that create separate charts for each time series--such as small multiples and horizon graphs--are generally more efficient for comparisons across time series with a large visual span.On the other hand, shared-space techniques--like standard line graphs--are typically more efficient for comparisons over smaller visual spans where the impact of overlap and clutter is reduced.